Using Formative and Summative Assessments in Data Mining to Predict Students‟ Final Grades

Abstract

The study intends to devise an earlier and more accurate analytical means of predicting the class of degree a student will graduate with.This will help in decision making and the design of academic programs and curriculum development; also it will help in student guidance and counseling.Because the earlier we can predict the class of degree a student is likely to graduate with, the better it will be for the student to keep it up or improve on his performance and this will consequently assist in mitigating and minimizing the student dropout, attrition, or dismissal from school after wasting reasonable amount of time without acquiring the certificate. Academic data of some Communication & InformationTechnology students; such as year of admission, year of completion, individual grades obtained from the courses he/she offered at a 1 year diploma program, 1 year advance diploma, and the class of degree he obtains from a 1year top-up degree program was introduced to a Classification Data Mining algorithms to extract a pattern and a model for students' final grade prediction.The study's result shows that timely completion of the first two programs, a high score in computer architecture course, programming, network, and discrete mathematics courses are determining factors that can be used to predict students' final grades at graduation.